Precision-Recall Curve Calculator

Generate and visualize a Precision-Recall curve for your binary classification model. Input your model's prediction scores and the corresponding true labels.

  • Prediction Scores: Comma-separated list of scores (e.g., 0.9, 0.2, 0.7, 0.1, 0.8). Scores typically range from 0 to 1.
  • True Labels: Comma-separated list of 0s (negative) and 1s (positive) corresponding to each prediction (e.g., 1, 0, 1, 0, 1).
  • The number of scores must match the number of labels.

Input Data

Higher scores indicate a higher probability of the positive class.
Corresponding actual labels (0 = Negative, 1 = Positive).

Precision-Recall Curve

Area Under PR Curve (AUPRC):
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Number of Positive Samples:
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Number of Negative Samples:
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